For data distribution, we split the data using three different distributions. One is the independent identically distribution (IID) which means that the whole training dataset is uniformly allocated to each client. The other two are non-IID distributions. For one, denoted as dn-IID, we use a Dirichlet distribution to split the data accordingly, as used in [1]}. For the other, we employ the method from [2]} to construct a pathological non-IID distribution (pn-IID), which first sorts the dataset according to the labels and then splits them into \(tN\)  parts where \(N\)  is the number of clients, with each client randomly choosing \(t\)  parts as its training set. In this case, each client only has data with \(t\)  different labels at most. We implement the settings in [2]} and set \(t=2\)  in our experiments.
